PirateBay Download | machine learning data science python kaggle pandas

machine learning data science python kaggle pandas

Type:: Other
Files:: 463
Size:: 9.1 GB
Uploaded On:: May 5, 2023, 4:20 p.m.
Added By:: tuts756
Seeders:: 13
Leechers:: 8
Info Hash:: 2573DF0A2B891C606621FA656140359C74749838

Description Hello there, Welcome to the ” Machine Learning & Data Science with Python, Kaggle & Pandas “ Course Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field. Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions. It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is built on top of another package named Numpy, which provides support for multi-dimensional arrays. Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. data analysis, pandas, numpy, numpy stack, numpy python, python data analysis, python, Python numpy, data visualization, pandas python, python pandas, python for data analysis, python data, data visualization. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack. Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python’s simple syntax is especially suited for desktop, web, and business applications. Python’s design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python’s large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks. Do you know data science needs will create 11.5 million job openings by 2026? Do you know the average salary is $100.000 for data science careers! Data Science Careers Are Shaping The Future Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand. If you want to learn one of the employer’s most request skills? If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python? If you are an experienced developer and looking for a landing in Data Science! In all cases, you are at the right place! We’ve designed for you “Machine Learning & Data Science with Python & Kaggle | A-Z” a straightforward course for Python Programming Language and Machine Learning. In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples. Also you will get to know the Kaggle platform step by step with hearth attack prediction kaggle project. Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-published data & code. Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detecting cancer cells. Kaggle has a massive community of data scientists who are always willing to help others with their data science problems. You will learn the Numpy and Pandas Python Programming Language libraries step by step. Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms. This Machine Learning course is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher). What is machine learning? Machine learning describes systems that make predictions using a model trained on real-world data. For example, let’s say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it’s fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model. Why we use a Python programming language in Machine learning? Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development. What is machine learning used for? Machine learning a-z is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions. Does Machine learning require coding? It’s possible to use machine learning data science without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It’s hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it What is the best language for machine learning? Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It’s useful to have a development environment such as Python so that you don’t need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets. What is a Kaggle? Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. How does Kaggle work? Every competition on Kaggle has a dataset associated with it and a goal you must reach (i.e., predict housing prices or detect cancer cells). You can access the data as often as possible and build your prediction model. Still, once you submit your solution, you cannot use it to make future submissions. This ensures that everyone is starting from the same point when competing against one another, so there are no advantages given to those with more computational power than others trying to solve the problem. Competitions are separated into different categories depending on their complexity level, how long they take, whether or not prize money is involved, etc., so users with varying experience levels can compete against each other in the same arena. What is a Pandas in Python? Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. What is Pandas used for? Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. What is difference between NumPy and pandas? NumPy library provides objects for multi-dimensional arrays, whereas Pandas is capable of offering an in-memory 2d table object called DataFrame. NumPy consumes less memory as compared to Pandas. Indexing of the Series objects is quite slow as compared to NumPy arrays. What are the different types of machine learning? Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled ‘spam’ or ‘not spam.’ That trained model could then identify new spam emails even from data it’s never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within. Is Machine learning a good career? Machine learning python is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience. What is the difference between machine learning and artifical intelligence? Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any “intelligent machine” that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward “true artificial intelligence” and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly. What skills should a machine learning engineer know? A python machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field. What is data science? We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods. Why would you want to take this course? Our answer is simple: The quality of teaching. OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading. When you enroll, you will feel the OAK Academy`s seasoned developers’ expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest. Video and Audio Production Quality All our videos are created/produced as high-quality video and audio to provide you the best learning experience. You will be, Seeing clearly Hearing clearly Moving through the course without distractions You’ll also get: Lifetime Access to The Course Fast & Friendly Support in the Q&A section Udemy Certificate of Completion Ready for Download We offer full support, answering any questions. If you are ready to learn Dive in now into the ” Machine Learning & Data Science with Python, Kaggle & Pandas “ Course Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples See you in the course! Who this course is for: Anyone who wants to start learning “Machine Learning” Anyone who needs a complete guide on how to start and continue their career with machine learning Students Interested in Beginning Data Science Applications in Python Environment People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python Anyone eager to learn python for data science and machine learning bootcamp with no coding background Anyone who plans a career in data scientist, Software developer whom want to learn python, Anyone interested in machine learning a-z People who want to become data scientist People who want to learn complete machine learning Requirements Basic knowledge of Python Programming Language Be Able To Operate & Install Software On A Computer Free software and tools used during the machine learning a-z course Determination to learn machine learning and patience. Motivation to learn the the second largest number of job postings relative program language among all others Data visualization libraries in python such as seaborn, matplotlib Curiosity for machine learning python Desire to learn Python Desire to learn matplotlib Desire to learn pandas and numpy Desire to learn machine learning a-z, complete machine learning Any device you can watch the course, such as a mobile phone, computer or tablet. Watching the lecture videos completely, to the end and in order. Nothing else! It’s just you, your computer and your ambition to get started today. LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device. Last Updated 4/2023

Get This Torrent

[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/35. Competition Section on Kaggle/2. Competitions on Kaggle Lesson 2.mp4 191.7 MB
TutsNode.net.txt 63 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/2. FAQ about Machine Learning, Data Science.html 15.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/1. Installations/2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html 155 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/34. First Contact with Kaggle/2. FAQ about Kaggle.html 10.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/17. First Contact with Machine Learning/5. FAQ regarding Machine Learning.html 6.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/17. First Contact with Machine Learning/4. FAQ regarding Python.html 6.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/1. Installations/4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4.2 KB
[TGx]Downloaded from torrentgalaxy.to .txt 585 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/2. NumPy Library Introduction/3. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/49. Extra/1. Machine Learning & Data Science with Kaggle, Pandas , Numpy.html 266 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/17. First Contact with Machine Learning/3. Machine Learning Project Files.html 254 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/10. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/8. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/7. Pandas Library Introduction/3. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/8. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/9. DataFrame Structures in Pandas Library/5. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/7. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/7. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/12. Multi-Indexed DataFrame Structures/4. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/7. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/10. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/15. Pivot Tables in Pandas Library/3. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/16. File Operations in Pandas Library/6. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/18. Evaluation Metrics in Machine Learning/5. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/19. Supervised Learning with Machine Learning/2. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/21. Bias Variance Trade-Off in Machine Learning/2. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/7. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/24. K Nearest Neighbors Algorithm in Machine Learning A-Z/5. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/7. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/28. Support Vector Machine Algorithm in Machine Learning A-Z/6. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/29. Unsupervised Learning with Machine Learning/2. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/30. K Means Clustering Algorithm in Machine Learning A-Z/6. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/31. Hierarchical Clustering Algorithm in machine learning data science/4. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/33. Recommender System Algorithm in Machine Learning A-Z/3. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/34. First Contact with Kaggle/6. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/35. Competition Section on Kaggle/3. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/36. Dataset Section on Kaggle/2. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/37. Code Section on Kaggle/4. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/38. Discussion Section on Kaggle/2. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/39. Other Most Used Options on Kaggle/4. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/40. Details on Kaggle/5. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/7. Pandas Library Introduction/2. Pandas Project Files Link.html 180 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/7. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/42. First Organization/4. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/43. Preparation For Exploratory Data Analysis (EDA)/5. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/44. Exploratory Data Analysis (EDA) - Uni-variate Analysis/6. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/15. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/12. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/9. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/48. Conclusion/2. Quiz.html 205 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/34. First Contact with Kaggle/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108 bytes
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html 108 bytes
.pad/0 241.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/35. Competition Section on Kaggle/1. Competitions on Kaggle Lesson 1.mp4 188.2 MB
.pad/1 821.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/37. Code Section on Kaggle/3. Examining the Code Section in Kaggle Lesson 3.mp4 159.8 MB
.pad/2 181.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/36. Dataset Section on Kaggle/1. Datasets on Kaggle.mp4 133.2 MB
.pad/3 813.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/34. First Contact with Kaggle/1. What is Kaggle.mp4 129.7 MB
.pad/4 256.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/6. Recognizing Variables In Dataset.mp4 126.9 MB
.pad/5 126.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/34. First Contact with Kaggle/5. Getting to Know the Kaggle Homepage.mp4 122.9 MB
.pad/6 120.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/1. Installations/1. Installing Anaconda Distribution for Windows.mp4 118.3 MB
.pad/7 711.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/1. First Step to the Project.mp4 117.2 MB
.pad/8 832.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/1. Installations/5. Installing Anaconda Distribution for Linux.mp4 114.8 MB
.pad/9 214.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/39. Other Most Used Options on Kaggle/2. Ranking Among Users on Kaggle.mp4 107.0 MB
.pad/10 2.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/20. Linear Regression Algorithm in Machine Learning A-Z/3. Linear Regression Algorithm With Python Part 2.mp4 106.9 MB
.pad/11 59.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/37. Code Section on Kaggle/2. Examining the Code Section in Kaggle Lesson 2.mp4 105.8 MB
.pad/12 199.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/3. Notebook Design to be Used in the Project.mp4 105.0 MB
.pad/13 35.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/18. Evaluation Metrics in Machine Learning/2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4 100.3 MB
.pad/14 731.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/18. Evaluation Metrics in Machine Learning/4. Machine Learning With Python.mp4 92.3 MB
.pad/15 757.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/43. Preparation For Exploratory Data Analysis (EDA)/4. Examining Statistics of Variables.mp4 91.4 MB
.pad/16 636.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/3. Aggregation Functions in Pandas DataFrames.mp4 90.7 MB
.pad/17 300.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4 90.7 MB
.pad/18 352.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/20. Linear Regression Algorithm in Machine Learning A-Z/5. Linear Regression Algorithm With Python Part 4.mp4 90.0 MB
.pad/19 5.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 88.1 MB
.pad/20 903.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/44. Exploratory Data Analysis (EDA) - Uni-variate Analysis/4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4 84.0 MB
.pad/21 986.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/40. Details on Kaggle/1. User Page Review on Kaggle.mp4 81.6 MB
.pad/22 438.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/3. Logistic Regression Algorithm with Python Part 2.mp4 81.5 MB
.pad/23 556.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/44. Exploratory Data Analysis (EDA) - Uni-variate Analysis/1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4 80.4 MB
.pad/24 612.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/37. Code Section on Kaggle/1. Examining the Code Section in Kaggle Lesson 1.mp4 79.5 MB
.pad/25 464.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/41. Introduction to Machine Learning with Real Hearth Attack Prediction Project/5. Examining the Project Topic.mp4 76.5 MB
.pad/26 485.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/20. Linear Regression Algorithm in Machine Learning A-Z/2. Linear Regression Algorithm With Python Part 1.mp4 76.2 MB
.pad/27 844.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/44. Exploratory Data Analysis (EDA) - Uni-variate Analysis/3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4 74.8 MB
.pad/28 240.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/40. Details on Kaggle/2. Treasure in The Kaggle.mp4 74.7 MB
.pad/29 350.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/2. Logistic Regression Algorithm with Python Part 1.mp4 72.2 MB
.pad/30 790.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/6. Operations in Numpy Library/2. Arithmetic Operations in Numpy.mp4 71.9 MB
.pad/31 129.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/20. Linear Regression Algorithm in Machine Learning A-Z/4. Linear Regression Algorithm With Python Part 3.mp4 70.3 MB
.pad/32 734.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4 68.1 MB
.pad/33 949.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/3. Null Values in Pandas Dataframes.mp4 67.0 MB
.pad/34 50.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/16. File Operations in Pandas Library/2. Data Entry with Csv and Txt Files.mp4 64.4 MB
.pad/35 659.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/42. First Organization/3. Initial analysis on the dataset.mp4 64.0 MB
.pad/36 19.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/1. Concatenating Pandas Dataframes Concat Function.mp4 63.9 MB
.pad/37 125.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/42. First Organization/1. Required Python Libraries.mp4 63.6 MB
.pad/38 438.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 60.1 MB
.pad/39 883.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/2. NumPy Library Introduction/2. The Power of NumPy.mp4 59.9 MB
.pad/40 137.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/24. K Nearest Neighbors Algorithm in Machine Learning A-Z/3. K Nearest Neighbors Algorithm with Python Part 2.mp4 59.4 MB
.pad/41 623.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/4. Hyperparameter Optimization (with GridSearchCV).mp4 58.8 MB
.pad/42 237.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/40. Details on Kaggle/4. What Should Be Done to Achieve Success in Kaggle.mp4 58.4 MB
.pad/43 594.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 57.3 MB
.pad/44 717.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/4. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 2.mp4 56.3 MB
.pad/45 741.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/6. Joining Pandas Dataframes Join() Function.mp4 56.1 MB
.pad/46 971.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/21. Bias Variance Trade-Off in Machine Learning/1. What is Bias Variance Trade-Off.mp4 55.0 MB
.pad/47 983.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/15. Pivot Tables in Pandas Library/2. Pivot Tables in Pandas Library.mp4 54.2 MB
.pad/48 787.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/44. Exploratory Data Analysis (EDA) - Uni-variate Analysis/5. Examining the Missing Data According to the Analysis Result.mp4 53.8 MB
.pad/49 223.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/8. Creating a New DataFrame with the Melt() Function.mp4 52.9 MB
.pad/50 120.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/8. Hyperparameter Optimization (with GridSearchCV).mp4 52.7 MB
.pad/51 334.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/39. Other Most Used Options on Kaggle/1. Courses in Kaggle.mp4 52.2 MB
.pad/52 855.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/5. Filling Null Values Fillna() Function.mp4 51.6 MB
.pad/53 395.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/1. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 1.mp4 49.3 MB
.pad/54 678.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/3. Decision Tree Algorithm with Python Part 2.mp4 49.0 MB
.pad/55 31.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/6. Most Applied Methods on Pandas Series.mp4 48.2 MB
.pad/56 826.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/25. Hyperparameter Optimization/2. Hyperparameter Optimization with Python.mp4 47.5 MB
.pad/57 546.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/28. Support Vector Machine Algorithm in Machine Learning A-Z/4. Support Vector Machine Algorithm with Python Part 3.mp4 47.3 MB
.pad/58 670.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/5. Logistic Regression Algorithm with Python Part 4.mp4 47.2 MB
.pad/59 855.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4 47.2 MB
.pad/60 861.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/8. Advanced Aggregation Functions Transform() Function.mp4 47.1 MB
.pad/61 925.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/4. Examining the Data Set 2.mp4 46.6 MB
.pad/62 442.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/6. Element Selection with Conditional Operations in.mp4 46.4 MB
.pad/63 647.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/1. Installations/3. Installing Anaconda Distribution for MacOs.mp4 46.3 MB
.pad/64 671.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/43. Preparation For Exploratory Data Analysis (EDA)/1. Examining Missing Values.mp4 45.8 MB
.pad/65 224.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/7. Fancy Indexing of Two-Dimensional Arrrays.mp4 45.7 MB
.pad/66 283.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/18. Evaluation Metrics in Machine Learning/3. Evaluating Performance Regression Error Metrics in Python.mp4 45.7 MB
.pad/67 296.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/2. NumPy Library Introduction/1. Introduction to NumPy Library.mp4 45.3 MB
.pad/68 714.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/43. Preparation For Exploratory Data Analysis (EDA)/2. Examining Unique Values.mp4 44.6 MB
.pad/69 449.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/4. Dealing with Outliers – Trtbps Variable Lesson 2.mp4 43.9 MB
.pad/70 88.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/34. First Contact with Kaggle/3. Registering on Kaggle and Member Login Procedures.mp4 43.5 MB
.pad/71 461.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/8. Creating NumPy Array with Random() Function.mp4 43.3 MB
.pad/72 720.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/2. Examining the Data Set 1.mp4 42.9 MB
.pad/73 124.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/3. Dealing with Outliers – Trtbps Variable Lesson 1.mp4 42.8 MB
.pad/74 167.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/12. Multi-Indexed DataFrame Structures/1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 42.7 MB
.pad/75 348.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/5. Decision Tree Algorithm with Python Part 4.mp4 42.5 MB
.pad/76 517.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/28. Support Vector Machine Algorithm in Machine Learning A-Z/3. Support Vector Machine Algorithm with Python Part 2.mp4 41.7 MB
.pad/77 288.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4 41.7 MB
.pad/78 305.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/3. Roc Curve and Area Under Curve (AUC).mp4 41.7 MB
.pad/79 323.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/9. Advanced Aggregation Functions Apply() Function.mp4 41.4 MB
.pad/80 586.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/39. Other Most Used Options on Kaggle/3. Blog and Documentation Sections.mp4 40.9 MB
.pad/81 92.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 40.7 MB
.pad/82 308.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/38. Discussion Section on Kaggle/1. What is Discussion on Kaggle.mp4 40.6 MB
.pad/83 359.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/6. Setting Index in Pandas DataFrames.mp4 39.7 MB
.pad/84 306.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/6. Logistic Regression Algorithm with Python Part 5.mp4 39.4 MB
.pad/85 658.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/1. Creating a Pandas Series with a List.mp4 39.2 MB
.pad/86 819.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/15. Pivot Tables in Pandas Library/1. Examining the Data Set 3.mp4 39.1 MB
.pad/87 900.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/27. Random Forest Algorithm in Machine Learning A-Z/3. Random Forest Algorithm with Pyhon Part 2.mp4 38.7 MB
.pad/88 270.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/27. Random Forest Algorithm in Machine Learning A-Z/2. Random Forest Algorithm with Pyhon Part 1.mp4 38.6 MB
.pad/89 418.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/4. Concatenating Numpy Arrays Concatenate() Function.mp4 38.4 MB
.pad/90 638.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 38.3 MB
.pad/91 709.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/40. Details on Kaggle/3. Publishing Notebooks on Kaggle.mp4 38.2 MB
.pad/92 810.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4 38.1 MB
.pad/93 955.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/32. Principal Component Analysis (PCA) in Machine Learning A-Z/1. Principal Component Analysis (PCA) Theory.mp4 38.0 MB
.pad/94 49.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/1. Loading a Dataset from the Seaborn Library.mp4 37.7 MB
.pad/95 288.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/28. Support Vector Machine Algorithm in Machine Learning A-Z/5. Support Vector Machine Algorithm with Python Part 4.mp4 37.6 MB
.pad/96 453.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/32. Principal Component Analysis (PCA) in Machine Learning A-Z/4. Principal Component Analysis (PCA) with Python Part 3.mp4 37.3 MB
.pad/97 744.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4 36.4 MB
.pad/98 646.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/5. Dealing with Outliers – Thalach Variable.mp4 36.2 MB
.pad/99 789.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/6. Dealing with Outliers – Oldpeak Variable.mp4 36.1 MB
.pad/100 938.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/1. Decision Tree Algorithm Theory.mp4 35.8 MB
.pad/101 249.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 35.7 MB
.pad/102 286.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/16. File Operations in Pandas Library/4. Outputting as an CSV Extension.mp4 35.7 MB
.pad/103 298.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/2. Numeric Variables – Target Variable (Analysis with FacetGrid) Lesson 2.mp4 35.6 MB
.pad/104 389.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/28. Support Vector Machine Algorithm in Machine Learning A-Z/2. Support Vector Machine Algorithm with Python Part 1.mp4 35.6 MB
.pad/105 445.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/31. Hierarchical Clustering Algorithm in machine learning data science/2. Hierarchical Clustering Algorithm with Python Part 1.mp4 35.5 MB
.pad/106 496.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4 35.5 MB
.pad/107 552.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/5. Assigning Value to Two-Dimensional Array.mp4 35.4 MB
.pad/108 602.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/7. Feature Scaling with the Robust Scaler Method.mp4 35.2 MB
.pad/109 824.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/24. K Nearest Neighbors Algorithm in Machine Learning A-Z/2. K Nearest Neighbors Algorithm with Python Part 1.mp4 35.1 MB
.pad/110 970.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/2. Visualizing Outliers.mp4 34.9 MB
.pad/111 137.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/4. Logistic Regression Algorithm with Python Part 3.mp4 34.8 MB
.pad/112 229.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/23. K-fold Cross-Validation in Machine Learning A-Z/2. K-Fold Cross-Validation with Python.mp4 34.7 MB
.pad/113 349.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/16. File Operations in Pandas Library/1. Accessing and Making Files Available.mp4 34.6 MB
.pad/114 384.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/4. Dropping Null Values Dropna() Function.mp4 34.5 MB
.pad/115 481.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/3. Slicing Two-Dimensional Numpy Arrays.mp4 34.3 MB
.pad/116 744.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/20. Linear Regression Algorithm in Machine Learning A-Z/1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4 34.1 MB
.pad/117 958.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/7. Pandas Library Introduction/1. Introduction to Pandas Library.mp4 33.9 MB
.pad/118 76.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/1. Adding Columns to Pandas Data Frames.mp4 33.6 MB
.pad/119 434.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/25. Hyperparameter Optimization/1. Hyperparameter Optimization Theory.mp4 33.1 MB
.pad/120 874.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/6. Decision Tree Algorithm with Python Part 5.mp4 32.7 MB
.pad/121 331.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/6. Operations in Numpy Library/3. Statistical Operations in Numpy.mp4 32.0 MB
.pad/122 21.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 31.8 MB
.pad/123 178.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/19. Supervised Learning with Machine Learning/1. What is Supervised Learning in Machine Learning.mp4 31.7 MB
.pad/124 320.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/2. Decision Tree Algorithm with Python Part 1.mp4 31.5 MB
.pad/125 467.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 31.4 MB
.pad/126 592.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/24. K Nearest Neighbors Algorithm in Machine Learning A-Z/4. K Nearest Neighbors Algorithm with Python Part 3.mp4 31.4 MB
.pad/127 623.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/12. Multi-Indexed DataFrame Structures/3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 31.3 MB
.pad/128 733.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/13. Structural Concatenation Operations in Pandas DataFrame/3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 30.5 MB
.pad/129 495.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/2. Cross Validation.mp4 30.2 MB
.pad/130 811.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/30. K Means Clustering Algorithm in Machine Learning A-Z/2. K Means Clustering Algorithm with Python Part 1.mp4 29.9 MB
.pad/131 64.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/7. Indexing and Slicing Pandas Series.mp4 29.9 MB
.pad/132 89.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 29.9 MB
.pad/133 120.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/7. Random Forest Algorithm.mp4 29.8 MB
.pad/134 228.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/11. Separating Data into Test and Training Set.mp4 29.8 MB
.pad/135 248.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/30. K Means Clustering Algorithm in Machine Learning A-Z/3. K Means Clustering Algorithm with Python Part 2.mp4 29.7 MB
.pad/136 356.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/1. Creating NumPy Array with The Array() Function.mp4 29.5 MB
.pad/137 525.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/1. Logistic Regression.mp4 29.3 MB
.pad/138 673.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/6. Advanced Aggregation Functions Aggregate() Function.mp4 29.2 MB
.pad/139 785.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/30. K Means Clustering Algorithm in Machine Learning A-Z/5. K Means Clustering Algorithm with Python Part 4.mp4 29.0 MB
.pad/140 997.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/31. Hierarchical Clustering Algorithm in machine learning data science/3. Hierarchical Clustering Algorithm with Python Part 2.mp4 28.9 MB
.pad/141 102.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/24. K Nearest Neighbors Algorithm in Machine Learning A-Z/1. K Nearest Neighbors Algorithm Theory.mp4 28.7 MB
.pad/142 337.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/48. Conclusion/1. Project Conclusion and Sharing.mp4 28.7 MB
.pad/143 354.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/31. Hierarchical Clustering Algorithm in machine learning data science/1. Hierarchical Clustering Algorithm Theory.mp4 28.6 MB
.pad/144 443.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4 28.4 MB
.pad/145 656.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/22. Logistic Regression Algorithm in Machine Learning A-Z/1. What is Logistic Regression Algorithm in Machine Learning.mp4 27.8 MB
.pad/146 164.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/30. K Means Clustering Algorithm in Machine Learning A-Z/4. K Means Clustering Algorithm with Python Part 3.mp4 27.8 MB
.pad/147 246.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/17. First Contact with Machine Learning/1. What is Machine Learning.mp4 27.6 MB
.pad/148 431.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/1. Dropping Columns with Low Correlation.mp4 26.8 MB
.pad/149 187.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/1. Indexing Numpy Arrays.mp4 26.6 MB
.pad/150 409.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/1. Reshaping a NumPy Array Reshape() Function.mp4 26.2 MB
.pad/151 869.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/32. Principal Component Analysis (PCA) in Machine Learning A-Z/2. Principal Component Analysis (PCA) with Python Part 1.mp4 26.0 MB
.pad/152 1002.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/9. DataFrame Structures in Pandas Library/4. Examining the Properties of Pandas DataFrames.mp4 25.9 MB
.pad/153 53.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/5. Decision Tree Algorithm.mp4 25.7 MB
.pad/154 307.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/7. Determining Distributions of Numeric Variables.mp4 25.2 MB
.pad/155 817.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/12. Multi-Indexed DataFrame Structures/2. Element Selection in Multi-Indexed DataFrames.mp4 24.6 MB
.pad/156 418.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/47. Modelling for machine learning/6. Support Vector Machine Algorithm.mp4 24.5 MB
.pad/157 513.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/14. Functions That Can Be Applied on a DataFrame/7. Advanced Aggregation Functions Filter() Function.mp4 24.5 MB
.pad/158 547.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/6. Operations in Numpy Library/4. Solving Second-Degree Equations with NumPy.mp4 24.2 MB
.pad/159 816.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/45. Exploratory Data Analysis (EDA) - Bi-variate Analysis/3. Categoric Variables – Target Variable (Analysis with Count Plot) Lesson 1.mp4 24.1 MB
.pad/160 896.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/9. Applying One Hot Encoding Method to Categorical Variables.mp4 24.1 MB
.pad/161 932.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/2. Creating NumPy Array with Zeros() Function.mp4 24.1 MB
.pad/162 961.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/8. Transformation Operations on Unsymmetrical Data.mp4 24.0 MB
.pad/163 2.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/33. Recommender System Algorithm in Machine Learning A-Z/1. What is the Recommender System Part 1.mp4 23.0 MB
.pad/164 1001.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/27. Random Forest Algorithm in Machine Learning A-Z/1. Random Forest Algorithm Theory.mp4 22.9 MB
.pad/165 112.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/9. DataFrame Structures in Pandas Library/1. Creating Pandas DataFrame with List.mp4 22.6 MB
.pad/166 450.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/2. Slicing One-Dimensional Numpy Arrays.mp4 22.3 MB
.pad/167 728.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/10. Element Selection Operations in DataFrame Structures/5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 22.1 MB
.pad/168 941.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/9. Properties of NumPy Array.mp4 22.0 MB
.pad/169 1012.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/28. Support Vector Machine Algorithm in Machine Learning A-Z/1. Support Vector Machine Algorithm Theory.mp4 21.8 MB
.pad/170 162.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/16. File Operations in Pandas Library/3. Data Entry with Excel Files.mp4 21.8 MB
.pad/171 171.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/6. Operations in Numpy Library/1. Operations with Comparison Operators.mp4 21.2 MB
.pad/172 854.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/5. Splitting One-Dimensional Numpy Arrays The Split.mp4 20.9 MB
.pad/173 91.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/6. Fancy Indexing of One-Dimensional Arrrays.mp4 20.5 MB
.pad/174 527.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/18. Evaluation Metrics in Machine Learning/1. Classification vs Regression in Machine Learning.mp4 19.9 MB
.pad/175 96.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/16. File Operations in Pandas Library/5. Outputting as an Excel File.mp4 19.8 MB
.pad/176 245.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/44. Exploratory Data Analysis (EDA) - Uni-variate Analysis/2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4 19.7 MB
.pad/177 276.2 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/4. Object Types in Series.mp4 19.5 MB
.pad/178 461.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/5. Examining the Primary Features of the Pandas Seri.mp4 18.9 MB
.pad/179 67.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/2. Creating a Pandas Series with a Dictionary.mp4 18.3 MB
.pad/180 726.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/4. Assigning Value to One-Dimensional Arrays.mp4 18.2 MB
.pad/181 815.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/33. Recommender System Algorithm in Machine Learning A-Z/2. What is the Recommender System Part 2.mp4 18.0 MB
.pad/182 42.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/23. K-fold Cross-Validation in Machine Learning A-Z/1. K-Fold Cross-Validation Theory.mp4 17.5 MB
.pad/183 550.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/30. K Means Clustering Algorithm in Machine Learning A-Z/1. K Means Clustering Algorithm Theory.mp4 17.1 MB
.pad/184 878.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/7. Sorting Numpy Arrays Sort() Function.mp4 17.0 MB
.pad/185 985.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/29. Unsupervised Learning with Machine Learning/1. Unsupervised Learning Overview.mp4 16.9 MB
.pad/186 81.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/9. Combining Fancy Index with Normal Slicing.mp4 16.5 MB
.pad/187 555.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/3. Creating NumPy Array with Ones() Function.mp4 15.8 MB
.pad/188 163.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/43. Preparation For Exploratory Data Analysis (EDA)/3. Separating variables (Numeric or Categorical).mp4 15.8 MB
.pad/189 168.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/9. DataFrame Structures in Pandas Library/3. Creating Pandas DataFrame with Dictionary.mp4 15.8 MB
.pad/190 179.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/11. Structural Operations on Pandas DataFrame/2. Removing Rows and Columns from Pandas Data frames.mp4 15.6 MB
.pad/191 439.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/2. Identifying the Largest Element of a Numpy Array.mp4 15.1 MB
.pad/192 878.1 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/26. Decision Tree Algorithm in Machine Learning A-Z/4. Decision Tree Algorithm with Python Part 3.mp4 14.7 MB
.pad/193 296.7 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/17. First Contact with Machine Learning/2. Machine Learning Terminology.mp4 14.0 MB
.pad/194 985.0 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/5. Indexing, Slicing, and Assigning NumPy Arrays/8. Combining Fancy Index with Normal Indexing.mp4 12.6 MB
.pad/195 362.3 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/6. Creating NumPy Array with Eye() Function.mp4 12.6 MB
.pad/196 436.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/9. DataFrame Structures in Pandas Library/2. Creating Pandas DataFrame with NumPy Array.mp4 12.1 MB
.pad/197 918.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/5. Creating NumPy Array with Arange() Function.mp4 12.1 MB
.pad/198 930.5 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/8. Series Structures in the Pandas Library/3. Creating Pandas Series with NumPy Array.mp4 12.0 MB
.pad/199 36.8 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/46. Preparation for Modelling in Machine Learning/10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4 11.5 MB
.pad/200 554.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/4. Creating NumPy Array with Full() Function.mp4 11.2 MB
.pad/201 826.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/4. Functions in the NumPy Library/3. Detecting Least Element of Numpy Array Min(), Ar.mp4 10.2 MB
.pad/202 830.4 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/42. First Organization/2. Loading the Dataset.mp4 10.0 MB
.pad/203 13.9 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/32. Principal Component Analysis (PCA) in Machine Learning A-Z/3. Principal Component Analysis (PCA) with Python Part 2.mp4 8.4 MB
.pad/204 584.6 KB
[TutsNode.net] - Machine Learning & Data Science with Python, Kaggle & Pandas/3. Creating NumPy Array in Python/7. Creating NumPy Array with Linspace() Function.mp4 7.3 MB

Details for: Machine Learning and Data Science with Python, Kaggle and Pandas

machine learning data science python kaggle pandas

Similar Posts: